Influence of the CONCERN Early Warning System on Unanticipated ICU Transfers, In-Hospital Mortality, and Length of Stay: Results from a Multi-site Pragmatic Randomized Controlled Clinical Trial

Presentation Time: 04:45 PM - 05:00 PM

Abstract Keywords: Nursing Informatics, Clinical Decision Support, Patient Safety, Machine Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics

Communicating Narrative Concerns Entered by RNs Early Warning System (CONCERN EWS) is a machine-learning predictive model that leverages nursing surveillance documentation patterns to predict deterioration risks for hospitalized patients. In a retrospective cohort study of 1,013 hospital encounters with unanticipated ICU transfers from a multi-site pragmatic randomized controlled trial, we assessed the influence of CONCERN EWS on in-hospital mortality and length of stay following unanticipated ICU transfers. Chi-square tests, t-tests, multivariate logistic regression, and generalized linear models were used. Our findings showed that patients who had unanticipated ICU transfers from acute care units with CONCERN EWS had a lower in-hospital mortality rate and a shorter average hospital stay than those transferred from units receiving usual care. These results suggest that CONCERN EWS significantly enhances situational awareness for care teams, improves communication, and effectively facilitates timely interventions, thereby streamlining care processes and improving patient outcomes.

Speaker(s):
Rachel Lee, PhD, RN
Columbia University

Author(s):
Kenrick Cato, PhD, RN, CPHIMS, FAAN - University of Pennsylvania/ Children's Hospital of Philadelphia; Patricia Dykes, PhD, RN, FAAN, FACMI - Brigham and Women's Hospital, Harvard Medical School; Graham Lowenthal, BA - Brigham and Women's Hospital; Jennifer Withall, PhD - Columbia University Department of Biomedical Informatics; Sandy Cho, Nurse Director - Newton-Wellesley Hospital; Haomiao Jia, PhD - Columbia University School of Nursing; Sarah Rossetti, RN, PhD - Columbia University Department of Biomedical Informatics;